This article examines the impact of an Academy Award nomination and award for best picture, best actor/actress, and best supporting actor/actress on a film's (i) market share of theaters, (ii) average revenue per screen, and (iii) its probability of survival. The model is estimated using weekly box-office data for a matched sample of nominated and non-nominated films. The results indicate substantial financial benefits for a nomination and award for best picture and best actor/actress. The structure of rewards is consistent with that found in two-stage, single-elimination tournaments.

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This paper examines the determinants of game-day attendance during Major League Baseball's 2002 spring training season in Florida. Our model of game-day attendance includes location, quality of game, and time and weather variables. A censored Tobit estimation procedure is used to estimate our model. Our results indicate that the quality of the game, average ticket price, and several location-specific factors affect attendance. Specifically, our results suggest that changes in income have no effect on attendance while increases in ticket prices cause reductions in attendance. Furthermore, the estimated price elasticity of demand for Major League Baseball during the spring training season is unitary. We also find that a number of factors unique to spring training, such as a nonresident fan base and split squads of players, significantly affect game-day attendance.

We investigate local economic impacts of shale gas development using the natural experiment of the discontinuity in regulation caused by New York's 2008 moratorium on fracking. Using county- and zip-code-level data for 2001–2013 to examine differences in New York and Pennsylvania counties before and after the moratorium, we find that shale gas development has a positive local impact on employment and wages in the natural resource, mining, and construction sectors and an offsetting reduction in employment in the manufacturing sector. Overall, we find no statistically significant local effects on total employment or on wages.

The majority of model-based forecasting efforts today rely on relatively simple techniques of estimation and the subjective adjustment of the model's results to produce forecasts. Published forecasts reflect to a great extent the judgment of the forecaster rather than what the model by itself has to say about the future. This paper examines the role judgment plays in the process of producing a macroeconometric forecast. The debate over the use of adjustment constants to alter the statistical results of a model is outlined and an empirical analysis of forecasts generated by the Michigan Quarterly Econometric Model of the US economy is presented using a unique data set which isolates the role of judgment in the forecasting process. KEY WORDS Judgment Macroeconometric models Adjustment constants Forecast accuracyFor over 50 years economists have been using econometric methods to produce models of economic behavior. Since the early 1950s, with Lawrence Klein's development of the first macroeconometric forecasting model of the US economy, the majority of model-based forecasting efforts have relied on relatively simple techniques of estimation and the subjective, ad hoc adjustment of the model's statistical results to produce forecasts of future events. While economic models have become much larger since that time, and the release of the forecasts more frequent, the standard operating procedures remain relatively unchanged-despite significant advancements in the theory and practice of econometrics and the availability of economic data. Published forecasts of economic behavior still reflect to a great extent the judgment and expertise of the forecaster rather than what the model itself has to say about the future given recent economic events. This is in direct contrast with modeling activities in other disciplines. In his evaluation of economic forecasting methods, Stephen McNees (1988) notes that 'Psychologists have long found that models . . . produce more accurate predictions than the unsupported, global intuition of acknowledged experts'. Meteorology is another example of a science which has made significant strides in the areas of modeling techniques, the use of higher-frequency data, and 'See the essays by Lawrence Klein (1982,1991) and Marjorie Brazer (1982) for a review of the early development of the econometric forecasting industry and the role played by the Economics Department at the University of Michigan.